One approach, particularly natural for enabling many different teams participating in the development of models without demographic information,
is to use proxy attributes as a replacement for the true sensitive attribute. The use of such proxy attributes has been widely used in domains such as health care [1]}, [2]} and finance [3]}. One can view a particular proxy attribute as a coarse attribute classifier that predicts the sensitive attribute information for a given example. For popular fairness metrics such as equalized odds or demographic parity [4]}, [5]}, [6]}, [7]}, the sensitive attribute information obtained from the classifier can then be used in conjunction with the predicted label information to get an estimate of the bias of a given model. The advantage of this approach is that it decouples the data requirement, and in particular, the attribute classifier can be trained on a separate dataset without any label information present. On the other hand, such an approach may produce very poor estimates, depending upon how the sources of data relate to one another.
